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dc.contributor.advisor
dc.contributor.editorCappellato L.
dc.contributor.editorFerro N.
dc.contributor.editorLosada D.E.
dc.contributor.editorMuller H.
dc.creatorPuertas E.
dc.creatorMoreno-Sandoval L.G.
dc.creatorPlaza-Del-Arco F.M.
dc.creatorAlvarado‑Valencia, Jorge Andres
dc.creatorPomares-Quimbaya A.
dc.creatorAlfonso Ureña-López L.
dc.date.accessioned2020-03-26T16:33:10Z
dc.date.available2020-03-26T16:33:10Z
dc.date.issued2019
dc.identifier.citationCEUR Workshop Proceedings; Vol. 2380
dc.identifier.issn16130073
dc.identifier.urihttps://hdl.handle.net/20.500.12585/9191
dc.description.abstractUnfortunately, in social networks, software bots or just bots are becoming more and more common because malicious people have seen their usefulness to spread false messages, spread rumors and even manipulate public opinion. Even though the text generated by users in social networks is a rich source of information that can be used to identify different aspects of its authors, not being able to recognize which users are truly humans and which are not, is a big drawback. In this work, we describe the properties of our multilingual classification model submitted for PAN2019 that is able to recognize bots from humans, and females from males. This solution extracted 18 features from the user's posts and applying a machine learning algorithm obtained good performance results. © 2019 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).eng
dc.format.mediumRecurso electrónico
dc.format.mimetypeapplication/pdf
dc.language.isoeng
dc.publisherCEUR-WS
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.sourcehttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85070510020&partnerID=40&md5=fcc69ef587023e644e71d9b5f6e5be01
dc.titleBots and gender profiling on twitter using sociolinguistic features notebook for PAN at CLEF 2019
dcterms.bibliographicCitationBerger, J.M., Morgan, J., The isis twitter census: Defining and describing the population of isis supporters on twitter (2015) The Brookings Project on US Relations with the Islamic World, 3 (20), pp. 4-11
dcterms.bibliographicCitationCai, C., Li, L., Zengi, D., Behavior enhanced deep bot detection in social media (2017) 2017 IEEE International Conference on Intelligence and Security Informatics (ISI), pp. 128-130
dcterms.bibliographicCitationClark, E.M., Williams, J.R., Jones, C.A., Galbraith, R.A., Danforth, C.M., Dodds, P.S., Sifting robotic from organic text: A natural language approach for detecting automation on twitter (2016) Journal of Computational Science, 16, pp. 1-7
dcterms.bibliographicCitationDaelemans, W., Kestemont, M., Manjavancas, E., Potthast, M., Rangel, F., Rosso, P., Specht, G., Zangerle, E., Overview of PAN 2019: Author profiling, celebrity profiling, cross-domain authorship attribution and style change detection (2019) Proceedings of the Tenth International Conference of the CLEF Association (CLEF 2019), , Crestani, F., Braschler, M., Savoy, J., Rauber, A., Müller, H., Losada, D., Heinatz, G., Cappellato, L., Ferro, N. eds Springer Sep
dcterms.bibliographicCitationDavis, C.A., Varol, O., Ferrara, E., Flammini, A., Menczer, F., Botornot: A system to evaluate social bots (2016) Proceedings of the 25th International Conference Companion on World Wide Web, pp. 273-274. , International World Wide Web Conferences Steering Committee
dcterms.bibliographicCitationDickerson, J.P., Kagan, V., Subrahmanian, V., Using sentiment to detect bots on twitter: Are humans more opinionated than bots? (2014) Proceedings of the 2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, pp. 620-627. , IEEE Press
dcterms.bibliographicCitationFerrara, E., Varol, O., Davis, C., Menczer, F., Flammini, A., The rise of social bots (2016) Communications of the ACM, 59 (7), pp. 96-104
dcterms.bibliographicCitationKrzywicki, A., Wobcke, W., Bain, M., Martinez, J.C., Compton, P., Data mining for building knowledge bases: Techniques, architectures and applications (2016) The Knowledge Engineering Review, 31 (2), pp. 97-123
dcterms.bibliographicCitationPotthast, M., Gollub, T., Wiegmann, M., Stein, B., TIRA integrated research architecture (2019) Information Retrieval Evaluation in a Changing World - Lessons Learned from 20 Years of CLEF, , Ferro, N., Peters, C. eds Springer
dcterms.bibliographicCitationRangel, F., Franco-Salvador, M., Rosso, P., A low dimensionality representation for language variety identification (2016) International Conference on Intelligent Text Processing and Computational Linguistics, pp. 156-169. , Springer
dcterms.bibliographicCitationRangel, F., Rosso, P., Overview of the 7th author profiling task at Pan 2019: Bots and gender profiling (2019) CLEF 2019 Labs and Workshops, Notebook Papers, , Cappellato, L., Ferro, N., Losada, D., Müller, H. eds CEUR-WS.org Sep
dcterms.bibliographicCitationRatkiewicz, J., Conover, M.D., Meiss, M., Gonçalves, B., Flammini, A., Menczer, F.M., Detecting and tracking political abuse in social media (2011) Fifth International AAAI Conference on Weblogs and Social Media
dcterms.bibliographicCitationVarol, O., Ferrara, E., Davis, C.A., Menczer, F., Flammini, A., Online human-bot interactions: Detection, estimation, and characterization (2017) Eleventh International AAAI Conference on Web and Social Media
dcterms.bibliographicCitationVarol, O., Ferrara, E., Menczer, F., Flammini, A., Early detection of promoted campaigns on social media (2017) EPJ Data Science, 6 (1), p. 13
dcterms.bibliographicCitationYang, K.C., Varol, O., Davis, C.A., Ferrara, E., Flammini, A., Menczer, F., (2019) Arming the Public with Ai to Counter Social Bots, , arXiv preprint
datacite.rightshttp://purl.org/coar/access_right/c_16ec
oaire.resourceTypehttp://purl.org/coar/resource_type/c_c94f
oaire.versionhttp://purl.org/coar/version/c_970fb48d4fbd8a85
dc.source.event20th Working Notes of CLEF Conference and Labs of the Evaluation Forum, CLEF 2019
dc.type.driverinfo:eu-repo/semantics/conferenceObject
dc.type.hasversioninfo:eu-repo/semantics/publishedVersion
dc.subject.keywordsAuthor profiling
dc.subject.keywordsBots profiling
dc.subject.keywordsComputational linguistic
dc.subject.keywordsGender profiling
dc.subject.keywordsSociolinguistic
dc.subject.keywordsUser profiling
dc.subject.keywordsCharacter recognition
dc.subject.keywordsClassification (of information)
dc.subject.keywordsComputational linguistics
dc.subject.keywordsLearning algorithms
dc.subject.keywordsLinguistics
dc.subject.keywordsMachine learning
dc.subject.keywordsSocial aspects
dc.subject.keywordsSocial networking (online)
dc.subject.keywordsSocial sciences computing
dc.subject.keywordsAuthor profiling
dc.subject.keywordsBots profiling
dc.subject.keywordsGender profiling
dc.subject.keywordsSociolinguistic
dc.subject.keywordsUser profiling
dc.subject.keywordsBotnet
dc.rights.accessrightsinfo:eu-repo/semantics/restrictedAccess
dc.rights.ccAtribución-NoComercial 4.0 Internacional
dc.identifier.instnameUniversidad Tecnológica de Bolívar
dc.identifier.reponameRepositorio UTB
dc.relation.conferencedate9 September 2019 through 12 September 2019
dc.type.spaConferencia
dc.identifier.orcid57202285682
dc.identifier.orcid57194828933
dc.identifier.orcid57191078469
dc.identifier.orcid8738428200
dc.identifier.orcid57203852380
dc.identifier.orcid56986551200


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Universidad Tecnológica de Bolívar - 2017 Institución de Educación Superior sujeta a inspección y vigilancia por el Ministerio de Educación Nacional. Resolución No 961 del 26 de octubre de 1970 a través de la cual la Gobernación de Bolívar otorga la Personería Jurídica a la Universidad Tecnológica de Bolívar.